riemannian metric

Learning Lineage-guided Geodesics with Finsler Geometry UAI 2026 Reconstructing Riemannian Metrics From Random Geometric Graphs COLT 2026 Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds AISTATS 2025 Preconditioned Riemannian Gradient Descent Algorithm for Low-Multilinear-Rank Tensor Completion ICML 2025 RiemStega: Covariance-Based Loss for Print-Proof Transmission of Data in Images WACV 2025 Data-free Neural Representation Compression with Riemannian Neural Dynamics ICML 2024 Geometric No-U-Turn Samplers: Concepts and Evaluation PGM 2024 Metric Flow Matching for Smooth Interpolations on the Data Manifold NeurIPS 2024 Achieving High Accuracy with PINNs via Energy Natural Gradient Descent ICML 2023 Estimating Riemannian Metric with Noise-Contaminated Intrinsic Distance NeurIPS 2023 Meta-Learning With a Geometry-Adaptive Preconditioner CVPR 2023 Riemannian Laplace approximations for Bayesian neural networks NeurIPS 2023 A prior-based approximate latent Riemannian metric AISTATS 2022 Lagrangian manifold Monte Carlo on Monge patches AISTATS 2022 Wasserstein Control of Mirror Langevin Monte Carlo COLT 2020 Latent Space Oddity: on the Curvature of Deep Generative Models ICLR 2018 Improved Graph Laplacian via Geometric Self-Consistency NeurIPS 2017 A Locally Adaptive Normal Distribution NeurIPS 2016 Time and Energy Optimal Path Planning in General Flows RSS 2016 A Statistical Model of Riemannian Metric Variation for Deformable Shape Analysis CVPR 2015 Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations ICML 2015 Area Preserving Brain Mapping CVPR 2013 Geometric Registration Based on Distortion Estimation ICCV 2013 A new metric on the manifold of kernel matrices with application to matrix geometric means NeurIPS 2012 Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment NeurIPS 2011